Your users will make mistakes. It’s inevitable. That’s what error messages are for—but so many companies fail to follow best practices, and they’re pissing off potential customers in the process.
So, how can we better design error messages to improve the user experience and increase conversions?
Friction is “the psychological resistance that your visitors experience when trying to complete an action.” It’s also a conversion killer.
You can optimize your value proposition or call to action buttons all you want, but if your sign-up flow contains too much friction, you’re leaving money on the table.
Analyzing the customer journey is pivotal to conversion optimization. But how do you track user journeys in a way that is digestible, visual, and useful?
With funnels, of course! Funnel tracking in Google Analytics is one of the best ways to identify—in detail—where you’re going wrong.
You’d like to think that you’re a completely rational person making completely rational decisions, right? It’s nice to believe that you haven’t made major life decisions based on how you were feeling.
Well, you have. Many times.
The classic graph for the product lifecycle is a sales curve that progresses through stages:
Each stage of the product lifecycle has implications for marketing. But an MBA-friendly curve rarely translates to reality. The goal of product lifecycle marketing is not to match the curve but to outline what may work best now and plan for the future.
When it comes to online imagery, it’s not so much about having images as making sure those images give the visitor a sense of texture, size, scale, detail, context, brand.
According to MDG Advertising, 67% of online shoppers rated high-quality images as being “very important” to their purchase decision, which was slightly more than “product specific information,” “long descriptions,” and “reviews and ratings”:
Propensity modeling lets us look at what people are likely to do—in the future.
My interest is in the future because I am going to spend the rest of my life there.
C.F. Kettering
When we look at data and analytics, we’re focused on the past. How did we do last quarter? What happened H1 2019? And how does that compare to H1 2018? How well did landing pages X, Y, and Z convert last Monday at 1:03 p.m.? (I’m kidding, I’m kidding.)
Data becomes more valuable when we use it to predict the future instead of just analyzing the past. That’s where propensity modeling comes in.
There’s a philosophical statistics debate in the A/B testing world: Bayesian vs. Frequentist.
This is not a new debate. Thomas Bayes wrote “An Essay towards solving a Problem in the Doctrine of Chances” in 1763, and it’s been an academic argument ever since.
There’s a popular user experience quote: “A user interface is like a joke. If you have to explain it, it’s not that good.” While clever, that statement is far from true.
User interfaces shouldn’t be complicated, but you can’t expect a new user to understand a new interface without any direction. Similarly, you can’t expect an existing user to understand an updated interface or a new feature without any help.
Perception isn’t always the same thing as reality, even when it comes to something as “objective” as your product’s value.
In fact, the perceived value of your product is fairly malleable. There are countless studies, as well as anecdotes, that support the notion that you can tweak small things to increase your product’s value perception.